We propose a topic-guided variational autoencoder (TGVAE) model for text
generation. Distinct from existing variational autoencoder (VAE) based
approaches, which assume a simple Gaussian prior for the latent code, our model
specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural
topic module. Each mixture component corresponds to a latent topic, which
provides guidance to generate sentences under the topic. The neural topic
module and the VAE-based neural sequence module in our model are learned
jointly. In particular, a sequence of invertible Householder transformations is
applied to endow the approximate posterior of the latent code with high
flexibility during model inference. Experimental results show that our TGVAE
outperforms alternative approaches on both unconditional and conditional text
generation, which can generate semantically-meaningful sentences with various
topics.

该研究提出了一种基于主题引导的变分自编码器（TGVAE）模型，该模型使用高斯混合模型作为先验，建立了神经主题模块对语料中的主题进行指导性生成。实验结果表明，该模型在无条件和有条件文本生成方面优于传统的变分自编码器方法。